Data Labelling Associate at Digital Divide Data (DDD Kenya)

The Data Labeling Associate plays a critical role in supporting AI and machine learning initiatives by delivering high-quality labeled datasets. This role demands precision, consistency, and efficiency to meet client expectations and project timelines. The successful candidate will demonstrate strong attention to detail, the ability to work both independently and collaboratively, and the adaptability to grow into a Quality Assurance (QA) capacity as needed.
Key Duties and Responsibilities

• Data Annotation: Accurately label and annotate datasets (images, video, text, LiDAR, etc.) in line with project guidelines and client requirements.
• Quality Assurance: Consistently check and validate work to ensure data accuracy. Demonstrate readiness to transition into a QA role as project needs evolve.
• Tool Proficiency: Efficiently use various data annotation platforms and software tools to complete assigned tasks.
• Documentation: Maintain clear and detailed annotation records, highlighting ambiguities and challenges encountered.
• Collaboration: Work closely with team members, supervisors, and cross-functional units to ensure shared understanding and consistency in project execution.
• Feedback Integration: Actively engage in training, implement feedback, and continuously refine annotation quality and speed.
• Continuous Learning: Stay informed on evolving AI/ML annotation tools, methods, and best practices to enhance performance.
• Time Management: Plan and prioritize tasks effectively to meet productivity goals and project deadlines.

Qualifications

Qualifications and Experience

• Education: High school diploma or equivalent required; diploma/certificate in IT, Data Science, or related fields is an added advantage.
• Experience: At least 2 years in data annotation (e.g., Ag Tech, autonomous systems, image/video annotation, LiDAR), data entry, or similar roles. Exposure to Generative AI projects is a strong plus.
• Technical Skills: Basic understanding of AI and machine learning concepts.
• Proficiency with computers and annotation software tools.